You run ads on Google, Facebook, and maybe even local radio. Customers walk through your door or call your showroom. But which ad brought them in? Most flooring retailers have no idea. You might be spending thousands on marketing that generates zero sales while ignoring the channels that actually work. Without proper attribution, you’re essentially throwing money at a wall and hoping something sticks.
Attribution models solve this problem by showing you exactly which marketing touchpoints deserve credit for your sales. This guide breaks down nine different attribution models, from simple single-touch approaches to advanced AI-driven systems. You’ll learn how each model works, when to use it, and see real examples specific to flooring retail. By the end, you’ll know which attribution approach fits your business so you can stop wasting ad dollars and start investing in what actually brings customers through your door.
1. AI driven attribution with IFDA.ai
Unlike the traditional types of attribution models that rely on preset rules, AI-driven attribution uses machine learning to analyze patterns across thousands of customer journeys. IFDA.ai built this technology specifically for flooring retailers, training algorithms on actual flooring purchase behavior rather than generic e-commerce data. The system continuously learns which touchpoints matter most at different stages of the customer decision process, adapting in real time as market conditions change.
What this model is
IFDA.ai’s attribution model represents a specialized application of artificial intelligence designed exclusively for the retail flooring industry. The system identifies and tracks three distinct customer segments: Planners (customers 3-6 months from purchase), Researchers (actively comparing options online), and Shoppers (visiting stores). This segmentation approach recognizes that your customers move through different stages before buying flooring, and each stage requires different marketing tactics and deserves different attribution credit.
How this model works
The platform starts by analyzing your market boundaries and competitive landscape through proprietary research. It then deploys AI algorithms that monitor consumer behavior signals across devices and platforms, identifying patterns that indicate genuine flooring purchase intent. When someone searches for "best hardwood for kitchens" at 2 AM, clicks your display ad the next morning, watches your video ad during lunch, and calls your store three days later, the AI weighs each interaction based on historical conversion data from similar customer journeys in the flooring industry.
Your campaign metrics update continuously as the AI learns which combinations of touchpoints drive store visits and sales. The system tracks impressions, clicks, calls, and actual purchases, then uses this data to optimize future ad delivery. Instead of guessing which platform works best, you see exactly how your Google ads, Facebook campaigns, and display advertising work together to move customers from awareness to purchase.
"AI attribution doesn’t just tell you what happened. It predicts what will work next based on thousands of similar customer journeys in your industry."
Pros and cons
AI-driven attribution delivers precision that rule-based models cannot match. You get insights specific to flooring retail rather than generic attribution data, and the system improves automatically without requiring you to adjust settings or rules. The platform handles complex multi-device customer journeys seamlessly, tracking the same person across their phone, tablet, laptop, and connected TV.
However, this approach requires sufficient data volume to produce reliable insights. You need a meaningful number of conversions for the AI to identify valid patterns. The 90-day test period IFDA.ai requires exists for exactly this reason. Additionally, you sacrifice some control compared to rule-based models where you manually set attribution weights. The AI makes decisions based on what works, not what you think should work.
When this model fits best
This model works best when you operate in a competitive market with complex customer journeys. If your typical customer researches for weeks, compares multiple flooring types, and visits several stores before deciding, AI attribution captures this complexity better than simple models. You should also have multiple active marketing channels (paid search, social media, display, video) since the AI excels at understanding how these channels work together.
Your business needs a baseline of regular traffic and conversions for the AI to analyze. If you only close five flooring jobs per month, you won’t generate enough data for reliable AI insights. The model also fits businesses willing to trust data over intuition. Some flooring retailers struggle when the AI recommends budget shifts that contradict their assumptions about what should work.
Example for flooring retailers
Consider a luxury vinyl retailer running campaigns across Google, Facebook, and programmatic display. A customer’s journey starts with a Facebook ad showing installation photos, continues two weeks later with Google searches for "waterproof flooring for basements," includes clicking several display ads over the next month, and concludes with a phone call after seeing a retargeted video ad. Traditional models would credit either the first Facebook ad or the final video ad. IFDA.ai’s system recognizes that the Google searches indicated high intent, the display ads maintained awareness during the research phase, and the video ad provided the final push. It assigns credit proportionally based on how similar patterns performed across hundreds of other conversions, then uses these insights to optimize future spending across all three channels.
2. First touch attribution model
First touch attribution represents the simplest approach among the various types of attribution models you can implement. This model assigns 100% of the credit for a sale or conversion to the very first marketing touchpoint a customer encounters. If someone sees your flooring ad on Facebook, later searches Google for your store, then calls you after seeing a retargeted display ad, first touch attribution credits the entire sale to that initial Facebook ad. You ignore everything that happens afterward.
What this model is
First touch attribution operates on a straightforward principle: the marketing channel that introduces your flooring business to a potential customer deserves full credit for any eventual purchase. This model treats your initial brand awareness efforts as the critical factor in the customer journey. When you implement this approach, you measure which channels bring new people into your sales funnel, regardless of how many additional touchpoints occur before they buy flooring from your store.
How this model works
Your analytics platform tracks the first interaction each customer has with your marketing efforts. When someone clicks your Google ad about laminate flooring, the system records Google Ads as their entry point. If that same person returns through organic search, clicks display ads, and watches your YouTube video before visiting your showroom, the model still credits the original Google ad click. The system ignores all subsequent touchpoints completely, maintaining focus solely on what brought the customer to you initially.
Pros and cons
This model excels at identifying which marketing channels attract new prospects to your flooring business. You gain clear visibility into your top-of-funnel performance, making budget decisions easier for awareness campaigns. The simplicity also means you need minimal setup and can start tracking immediately without complex configuration.
"First touch attribution tells you what gets customers interested, but it tells you nothing about what convinces them to buy."
However, you completely miss the impact of nurturing and conversion touchpoints. Your retargeting campaigns, email marketing, and sales calls receive zero credit even though they often determine whether someone buys from you instead of a competitor. This creates a misleading picture of your marketing effectiveness, especially in flooring retail where customers typically research for weeks before making a purchase decision.
When this model fits best
You should use first touch attribution when your primary goal involves brand awareness and bringing new prospects into your funnel. This model works well for flooring retailers launching in new markets where you need to understand which channels successfully introduce your business to potential customers. It also fits situations where you have a relatively short sales cycle with few touchpoints between initial contact and purchase.
Example for flooring retailers
A carpet retailer runs Google Ads and Facebook campaigns simultaneously to promote a spring sale. A homeowner first clicks a Facebook ad showing before-and-after installation photos, which leads them to the retailer’s website. Over the next three weeks, that homeowner searches "carpet installation near me" and clicks the retailer’s Google ad twice, visits the website directly, and finally calls to schedule a consultation after seeing a retargeted display ad. First touch attribution credits the entire sale to Facebook, indicating that social media advertising successfully attracts new customers. The retailer now knows Facebook works for initial awareness, but this model reveals nothing about whether the Google ads or retargeting influenced the final purchase decision.
3. Last touch attribution model
Last touch attribution takes the opposite approach from first touch by assigning 100% credit to the final marketing interaction before a customer converts. When a homeowner sees your flooring ad on Facebook, searches for your store on Google weeks later, receives your email newsletter, and finally calls after clicking a retargeted display ad, this model credits the entire sale to that last display ad. You treat the closing touchpoint as the only one that mattered.
What this model is
This attribution approach focuses exclusively on conversion-driving touchpoints rather than awareness efforts. Last touch attribution operates on the assumption that the final marketing interaction pushed the customer over the finish line, making it the most important element in your marketing mix. Among the various types of attribution models available, this one remains the most common default setting in analytics platforms like Google Analytics because it offers clear, straightforward reporting.
How this model works
Your tracking system records every marketing touchpoint but only assigns value to the last one before conversion. If a customer interacts with your flooring business through five different channels over two months, the system ignores the first four completely. When that customer finally visits your showroom or calls for a consultation, the platform credits whichever marketing channel they used immediately before taking action. The model provides a simple answer to which touchpoint closed the deal.
Pros and cons
Last touch attribution excels at identifying conversion-focused channels like retargeting campaigns, email promotions, and paid search ads that capture high-intent customers. You gain clarity on which marketing efforts drive immediate action, making this model valuable for optimizing bottom-of-funnel activities. The simplicity also means you can implement it quickly without complex configuration or analysis.
"Last touch attribution shows you what closed the sale, but it ignores everything that built interest and trust beforehand."
The critical weakness lies in completely disregarding awareness and consideration touchpoints. Your initial ads, content marketing, and brand-building efforts receive zero credit even though they introduced customers to your flooring business and kept you top-of-mind during their research phase. This creates particularly misleading results in flooring retail where customers typically research for weeks before making a purchase decision.
When this model fits best
You should implement last touch attribution when your primary focus involves optimizing conversion rates rather than building awareness. This model works well for flooring retailers with strong brand recognition in their local market, where customers already know about your business and you mainly need to capture them at decision time. It also fits situations with short sales cycles where customers make quick decisions with minimal research.
Example for flooring retailers
A tile retailer targets homeowners actively searching for "tile installation contractors." A customer first discovers the retailer through a Google search ad about porcelain tile, visits the website but leaves without converting. Two weeks later, that customer sees the retailer’s Facebook post about a limited-time installation discount, clicks through to the website, and browses product galleries. Five days after that, a retargeted display ad reminds them about the discount. The customer clicks that ad and immediately calls to schedule a consultation. Last touch attribution credits the retargeted display ad with the entire sale, suggesting that retargeting campaigns drive conversions. While this insight helps optimize retargeting budget, it completely ignores how the initial Google ad and Facebook post built awareness and interest.
4. Last non direct touch attribution
Last non direct touch attribution refines the last touch model by filtering out direct traffic and instead crediting the last marketing channel that actually brought the customer to your flooring business. When a homeowner clicks your Google ad, visits your website later by typing your URL directly, then converts, this model credits Google rather than the direct visit. You recognize that direct traffic often represents customers who already know your brand through previous marketing efforts rather than discovering you organically.
What this model is
This model addresses a fundamental weakness in standard last touch attribution by distinguishing between earned marketing touchpoints and direct website visits. Direct traffic occurs when customers type your flooring store’s URL into their browser, click a bookmark, or arrive through an unknown source. Last non direct touch attribution excludes these direct visits from receiving credit because they typically result from prior marketing exposure rather than representing genuine discovery moments. Among the types of attribution models available, this approach provides a more accurate view of your marketing effectiveness than basic last touch while maintaining simplicity.
How this model works
Your analytics platform tracks all customer interactions but applies specific logic when assigning credit. If a customer’s journey includes multiple marketing touchpoints followed by a direct visit before conversion, the system looks backward to find the last non direct interaction. When someone clicks your Facebook ad on Monday, searches for your store on Google Tuesday, types your URL directly on Wednesday, and converts that same day, the model credits Google search rather than the direct visit. The system treats direct traffic as navigation rather than discovery, recognizing that customers who type your URL already learned about your flooring business through some marketing channel.
Pros and cons
This attribution approach eliminates the bias that direct traffic creates in standard last touch models. You gain more accurate insights into which paid and organic channels actually drive conversions rather than seeing "direct" dominate your reports. The model remains simple to implement and understand, requiring no complex configuration beyond what most analytics platforms offer by default.
"Last non direct attribution recognizes that customers who type your URL directly already know you exist, but it still ignores everything except the final marketing touchpoint."
However, you still overlook all earlier marketing efforts that built awareness and consideration. Your initial ads and content receive zero credit even when they introduced customers to your flooring store and influenced their research process. The model also struggles when customers have long gaps between touchpoints, potentially crediting channels that merely maintained awareness rather than driving the actual purchase decision.
When this model fits best
You should implement this model when direct traffic dominates your conversion reports and you need clearer visibility into which marketing channels actually drive customer acquisition. This approach works well for flooring retailers with strong local brand recognition where customers frequently return by typing your URL after initial discovery through marketing channels. It also fits businesses running multiple paid advertising campaigns where you need to distinguish performance between channels without the noise of direct traffic inflating certain numbers.
Example for flooring retailers
A hardwood flooring retailer runs Google Ads and email campaigns targeting homeowners researching renovation options. A customer first clicks a Google search ad for "solid hardwood flooring near me" and browses the retailer’s website for twenty minutes. Three days later, the customer receives an email newsletter featuring installation tips and a limited-time discount code. That same afternoon, the customer types the retailer’s URL directly into their browser, uses the discount code, and requests a consultation. Last non direct attribution credits the email campaign rather than the direct visit, correctly recognizing that the email prompted the customer to return and convert. This gives the retailer accurate insight into email performance, though the model still ignores how the initial Google ad introduced the customer to the business.
5. Linear multi touch attribution
Linear multi touch attribution distributes credit equally across all marketing touchpoints in a customer’s journey, treating every interaction as equally important. When a homeowner clicks your Google ad, visits your Facebook page, reads your blog post, watches your YouTube video, and finally calls your showroom, this model assigns 20% credit to each touchpoint rather than favoring the first or last interaction. You acknowledge that multiple channels work together to move customers toward a purchase decision.
What this model is
This model represents a fundamental shift from single-touch approaches by recognizing that customers interact with your flooring business through multiple channels before buying. Linear attribution treats your entire marketing ecosystem as equally valuable, operating on the principle that every touchpoint contributes something meaningful to the customer journey. Among the types of attribution models that consider multiple interactions, linear attribution offers the simplest calculation method because it requires no complex weighting formulas or assumptions about which touchpoints matter more.
How this model works
Your analytics platform tracks every marketing interaction a customer has with your flooring business from their first exposure to final conversion. When someone converts, the system divides the attribution credit evenly among all recorded touchpoints. If a customer interacted with your marketing five times before requesting a consultation, each interaction receives exactly 20% of the credit. The platform calculates this percentage automatically by dividing 100% by the total number of touchpoints, ensuring every channel gets proportional recognition regardless of its position in the customer journey.
Pros and cons
Linear attribution provides a balanced view of your entire marketing operation rather than overstating the importance of awareness or conversion touchpoints. You gain visibility into how different channels work together throughout the customer journey, helping you understand the cumulative effect of your marketing mix. This approach prevents you from cutting budgets for mid-funnel activities that single-touch models ignore completely.
"Linear attribution treats all touchpoints equally, which means it can undervalue critical moments that actually drive purchase decisions."
The equal weighting creates problems when some touchpoints genuinely matter more than others. Your initial awareness ad and final retargeting campaign receive the same credit even though they serve completely different purposes and likely have different impacts on conversion. This model also inflates the value of minor interactions like social media likes or brief website visits by treating them the same as high-intent touchpoints like calling your store or visiting your showroom.
When this model fits best
You should implement linear attribution when you want a comprehensive overview of your marketing performance without making assumptions about which touchpoints deserve more credit. This model works well for flooring retailers with complex customer journeys involving many touchpoints across different channels. It fits situations where you need to justify budget allocation across your entire marketing mix rather than concentrating spending on just awareness or conversion activities.
Example for flooring retailers
A vinyl flooring retailer runs integrated campaigns across five channels: Google Ads, Facebook, email marketing, display advertising, and YouTube. A customer first sees a Facebook ad about waterproof flooring options, then clicks a Google search ad two weeks later while researching "best vinyl for kitchens." That customer subscribes to the retailer’s email list and receives a newsletter with installation guides. Three days later, a display retargeting ad reminds them about the retailer, and they finally watch a YouTube video showing installation before calling to schedule a consultation. Linear attribution assigns 20% credit to each of these five touchpoints, showing the retailer that all five channels contributed to the conversion. This balanced view helps justify maintaining budget across the entire marketing mix rather than cutting channels that single-touch models would ignore.
6. Time decay attribution model
Time decay attribution assigns progressively more credit to marketing touchpoints that occur closer to the conversion event, recognizing that recent interactions typically have stronger influence on purchase decisions than older ones. When a homeowner clicks your flooring ad eight weeks before buying, then searches Google a month later, reads your blog two weeks after that, and finally calls after seeing a retargeted ad yesterday, this model gives the most credit to yesterday’s ad and the least to the initial click eight weeks ago. You acknowledge that customer interest and intent increase as they move toward a purchase decision.
What this model is
This attribution approach operates on a decay principle where each touchpoint’s value diminishes over time based on how far removed it sits from the actual conversion. Time decay attribution recognizes that while early touchpoints introduce customers to your flooring business, the interactions happening days or hours before purchase typically carry more weight in the final decision. Among the types of attribution models that consider multiple touchpoints, time decay offers a middle ground between giving all credit to the last interaction and distributing it equally across everything.
How this model works
Your analytics platform applies a mathematical decay function that reduces attribution credit as you move backward from the conversion event. Most platforms use a seven-day half-life by default, meaning a touchpoint that occurred seven days before conversion receives half the credit of a same-day interaction. If a customer clicked your Google ad 14 days before converting, that click receives 25% of the credit compared to an interaction on the conversion day. The system calculates these percentages automatically, creating a weighted distribution that favors recent activity while still acknowledging earlier touchpoints contributed something to the customer journey.
Pros and cons
Time decay attribution recognizes that customer intent intensifies as they approach a purchase decision, giving appropriate weight to the marketing efforts that occur during high-consideration periods. You gain insight into which late-stage touchpoints effectively close sales while still maintaining visibility into earlier awareness activities. This model particularly helps flooring retailers understand how retargeting campaigns and follow-up emails perform compared to initial discovery channels.
"Time decay attribution assumes recent touchpoints matter most, but sometimes an early interaction plants the seed that drives everything else."
The model’s core assumption creates problems when early touchpoints significantly influence the entire customer journey even though they occurred weeks before conversion. Your initial brand awareness campaign might introduce a homeowner to luxury vinyl flooring as a viable option, fundamentally shaping their entire research process, yet this model gives that crucial moment minimal credit. The time-based decay also ignores the nature of each interaction, treating a quick social media like the same as an hour-long showroom visit if they occurred on the same day.
When this model fits best
You should implement time decay attribution when your flooring business experiences typical sales cycles of several weeks where customers gradually increase their engagement as they move toward a purchase decision. This model works particularly well for retailers with active retargeting and email nurturing campaigns designed to re-engage customers who showed initial interest but haven’t converted yet. It fits situations where you want to balance recognition of your entire funnel while giving more weight to conversion-driving activities.
Example for flooring retailers
A laminate flooring retailer tracks a customer who first clicks a Google display ad showing installation photos 45 days before requesting a consultation. That customer returns through organic search 30 days out and spends time reading comparison guides. At the 15-day mark, they open an email newsletter about spring promotions. Seven days before converting, they click a retargeted Facebook ad and browse product pages. Finally, they see a Google search ad the day before calling to schedule their consultation. Time decay attribution assigns approximately 40% credit to the final Google search ad, 25% to the Facebook retargeting, 15% to the email, 12% to organic search, and 8% to the initial display ad. This distribution shows the retailer that late-stage touchpoints drive conversions while early awareness efforts still contribute meaningful value to the overall journey.
7. U shaped attribution model
U shaped attribution assigns 40% of conversion credit to both the first and last touchpoints in a customer’s journey while distributing the remaining 20% evenly among all middle interactions. When a homeowner clicks your Google ad introducing them to bamboo flooring, researches through five different channels over the next month, and finally converts after seeing a retargeted email offer, this model gives substantial credit to both that initial Google ad and the final email while acknowledging the research touchpoints in between. You recognize that both introducing customers to your flooring business and closing the sale deserve significant attribution.
What this model is
This attribution approach balances recognition of awareness and conversion activities by heavily weighting both ends of the customer journey. U shaped attribution operates on the principle that the first interaction introduces your flooring brand and shapes the customer’s entire research process, while the last touchpoint provides the final push toward conversion. The model gets its name from the U-shaped curve you see when plotting attribution percentages across the customer journey, with peaks at the beginning and end and a valley in the middle. Among the various types of attribution models that consider multiple touchpoints, U shaped offers a balanced perspective that values both customer acquisition and conversion optimization.
How this model works
Your analytics platform tracks all customer interactions from initial contact through final conversion, then applies the predetermined weighting formula. The system assigns 40% credit to whichever marketing channel first brought the customer to your flooring business, another 40% to the final touchpoint before conversion, and divides the remaining 20% equally among all interactions that occurred in between. If a customer had seven total touchpoints, the first receives 40%, the seventh receives 40%, and the middle five each get 4%. This calculation happens automatically based on your tracking data, creating a distribution that highlights both ends of the journey.
Pros and cons
U shaped attribution recognizes the critical importance of both attracting new customers and converting them into buyers, preventing you from overlooking either awareness or closing activities. You gain clear visibility into which initial touchpoints introduce customers to your flooring options and which final interactions seal the deal. This model helps you maintain balanced budgets between top-of-funnel and bottom-of-funnel marketing efforts.
"U shaped attribution values the beginning and end of the customer journey, but it treats all middle touchpoints as equally unimportant regardless of their actual impact."
The model’s weakness lies in undervaluing mid-funnel activities that nurture customer interest and build consideration. Your educational content, product comparison tools, and research-phase retargeting campaigns receive minimal credit even when they play crucial roles in keeping customers engaged. The arbitrary 40-40-20 split also assumes every customer journey follows the same pattern, ignoring situations where middle touchpoints actually drive the conversion decision.
When this model fits best
You should implement U shaped attribution when you operate a flooring business that invests heavily in both customer acquisition and conversion optimization. This model works well for retailers running brand awareness campaigns to attract new prospects while simultaneously maintaining aggressive retargeting and email follow-up systems to convert them. It fits situations where you want to justify budgets for both ends of your funnel without completely ignoring nurturing activities.
Example for flooring retailers
A tile retailer targets homeowners planning kitchen renovations through multiple channels. A customer first clicks a Google display ad featuring modern tile designs, introducing them to the retailer’s luxury collections. Over the next six weeks, that customer visits the website through organic search twice, clicks two Facebook retargeting ads, reads three blog posts about tile selection, and watches a YouTube video about installation. Finally, they receive an email campaign about a consultation discount and immediately call to schedule an appointment. U shaped attribution assigns 40% credit to the initial Google display ad for creating awareness, 40% to the email campaign for closing the sale, and splits the remaining 20% across the five middle touchpoints (4% each). This distribution shows the retailer that their awareness and conversion efforts both work effectively, though it minimizes the value of their content marketing and retargeting that sustained interest throughout the journey.
8. W shaped attribution model
W shaped attribution distributes credit across three critical touchpoints in the customer journey by assigning 30% to the first interaction, 30% to the lead creation moment, and 30% to the final conversion touchpoint, while splitting the remaining 10% among all other interactions. When a homeowner first discovers your flooring business through a Facebook ad, later fills out a contact form after watching your installation video, researches further through multiple channels, and finally converts after a sales call, this model recognizes all three key moments as equally important. You acknowledge that attracting attention, capturing contact information, and closing the sale each represent distinct achievements that deserve substantial credit.
What this model is
This attribution approach extends the U shaped model by adding recognition for lead creation as a pivotal moment in the customer journey. W shaped attribution operates on the principle that converting anonymous website visitors into identified leads with contact information represents a critical milestone that deserves equal weight alongside initial awareness and final conversion. The model gets its name from the W-shaped curve created when plotting attribution percentages across the customer journey, with three peaks at the first touch, lead creation, and conversion. Among the various types of attribution models available, W shaped provides the most detailed recognition of distinct funnel stages while maintaining relatively simple implementation.
How this model works
Your analytics platform identifies three specific events in each customer’s journey: the initial marketing touchpoint that brought them to your flooring business, the interaction that converted them from anonymous visitor to identified lead (typically a form submission or phone call), and the final touchpoint before they became a paying customer. The system assigns exactly 30% credit to each of these three events regardless of how many total touchpoints occurred. The remaining 10% gets divided equally among all other interactions between these key moments. If a customer had eight total touchpoints with the three key moments plus five additional interactions, those five middle touchpoints each receive 2% credit. This calculation happens automatically based on your CRM and analytics data.
Pros and cons
W shaped attribution provides comprehensive recognition of the three most important transitions in your customer journey: awareness, lead capture, and conversion. You gain visibility into which marketing channels excel at each distinct stage rather than treating your funnel as a simple beginning and end. This model particularly helps flooring retailers understand how content offers, webinars, and consultation forms perform at converting visitors into qualified leads.
"W shaped attribution recognizes three key moments, but it assumes lead creation always happens at a consistent point in every customer’s journey."
The model’s complexity creates problems when customer journeys don’t follow the expected pattern. Some flooring customers call directly after seeing your first ad without filling out forms, while others submit multiple contact requests at different stages. The arbitrary 30-30-30-10 split also assumes these three moments always deserve equal weight, ignoring situations where lead nurturing activities between form submission and purchase actually drive the conversion decision.
When this model fits best
You should implement W shaped attribution when your flooring business operates a structured sales funnel with clearly defined stages including lead capture mechanisms like consultation request forms, design tool submissions, or quote requests. This model works well for retailers with dedicated sales teams who follow up on leads through phone calls and showroom appointments, creating distinct touchpoints between initial contact and final purchase. It fits situations where you want to optimize both lead generation and lead conversion activities rather than focusing exclusively on either awareness or closing.
Example for flooring retailers
A hardwood flooring retailer tracks a customer who first clicks a Google search ad about refinishing services, introducing them to the business. Three days later, that customer watches a YouTube video tutorial about hardwood maintenance and submits a contact form requesting a consultation, creating the lead creation touchpoint. Over the next two weeks, the customer receives three email follow-ups, clicks a Facebook retargeting ad, and reads two blog posts about wood species selection. Finally, a sales representative calls to schedule an in-home consultation, and the customer agrees to move forward. W shaped attribution assigns 30% credit to the initial Google search ad for awareness, 30% to the YouTube video for lead creation, and 30% to the sales call for closing, while splitting the remaining 10% across the three emails, Facebook ad, and blog posts (approximately 1.7% each). This distribution shows the retailer that all three stages require investment, though it minimizes the value of the email nurturing sequence that maintained engagement.
9. Data driven and algorithmic attribution
Data driven attribution uses machine learning algorithms to analyze thousands of actual customer journeys and assign credit based on statistical patterns rather than predetermined rules. Instead of manually deciding whether your first ad or last email deserves more credit, these algorithms examine every conversion in your flooring business and calculate which touchpoints historically correlate with successful sales. The system identifies patterns across customer behavior, weighting each interaction based on how frequently it appears in converting versus non-converting journeys. This approach represents the most sophisticated option among the various types of attribution models because it adapts continuously as your marketing performance and customer behavior evolve.
What this model is
Data driven attribution represents an algorithmic approach that removes human assumptions from the attribution process entirely. The system analyzes your actual conversion data to determine which marketing touchpoints statistically increase the likelihood of a sale occurring. Unlike rule-based models that assign fixed percentages to specific positions in the customer journey, data driven models calculate unique attribution weights for each touchpoint based on how that interaction correlates with conversion outcomes across your entire dataset. These weights change automatically as the algorithm processes new conversion data, adapting to shifts in customer behavior, seasonal trends, and marketing effectiveness.
How this model works
Your analytics platform collects data about every marketing interaction across all customer journeys, including both conversions and non-conversions. The algorithm compares patterns between customers who bought flooring and those who didn’t, identifying which touchpoints appear more frequently in successful journeys. When someone clicks your Google ad, reads your blog, watches your video, and converts, the system doesn’t simply divide credit equally. Instead, it examines hundreds or thousands of similar journeys to calculate how much each touchpoint type actually influences conversion probability. The algorithm applies sophisticated statistical techniques like logistic regression or Shapley values to determine contribution scores, then assigns attribution percentages based on these calculations.
"Data driven attribution reveals what actually drives conversions rather than what you assume should work based on touchpoint position."
Pros and cons
This approach delivers precision impossible with rule-based models because it bases attribution on actual performance data specific to your flooring business and market. You discover which touchpoints genuinely influence purchase decisions rather than guessing based on position in the customer journey. The system automatically adapts to changes in customer behavior without requiring you to manually adjust attribution rules or percentages.
However, data driven attribution requires substantial conversion volume to produce statistically reliable results. You need hundreds of conversions for the algorithm to identify valid patterns rather than random correlations. Most platforms require minimum thresholds before activating data driven models. The black box nature also means you sacrifice transparency, as complex algorithms make attribution decisions without clearly explaining why specific touchpoints received their assigned credit.
When this model fits best
You should implement data driven attribution when your flooring business generates sufficient conversion volume to support statistical analysis, typically at least 200-300 conversions per month across your marketing channels. This model works best for retailers operating complex multi-channel campaigns where customer journeys vary significantly and rule-based assumptions break down. It fits situations where you want maximum accuracy in attribution reporting and possess the technical infrastructure to integrate algorithmic models with your analytics platform.
Example for flooring retailers
A multi-location flooring retailer runs campaigns across eight different channels with over 400 monthly conversions. A typical customer interacts with the brand through a Google search ad, Facebook carousel showing product categories, two display retargeting campaigns, an email with installation guides, organic search visits, and a final remarketing video before requesting a consultation. Rule-based models would assign credit based on predetermined formulas, but data driven attribution analyzes this journey against thousands of similar patterns. The algorithm determines that Google search ads correlate with 28% higher conversion rates, display retargeting shows 15% lift, and the email contributes 12% increased likelihood of conversion, assigning credit proportionally based on these calculated impact scores rather than arbitrary position-based rules.
Final thoughts
Understanding the nine types of attribution models covered in this guide gives you the foundation to make smarter marketing decisions for your flooring business. Each model serves different purposes, from first touch attribution revealing which channels attract new prospects to AI-driven systems that adapt based on actual conversion patterns. Your choice depends on sales cycle length, customer journey complexity, and the volume of conversions your business generates monthly.
Most flooring retailers default to whatever their analytics platform provides, typically last touch attribution, without realizing this approach completely ignores awareness and nurturing touchpoints that introduce customers to your business. Moving beyond basic models reveals how your marketing channels work together rather than competing for credit based on arbitrary position rules. The right attribution approach transforms vague marketing metrics into actionable insights about customer behavior.
Start with the attribution model that matches your current marketing maturity and data volume, then evolve toward more sophisticated approaches as your business grows. Learn how IFDA.ai’s specialized attribution technology identifies which marketing touchpoints actually drive flooring sales in your market, giving you the insights needed to stop wasting budget on ineffective channels.
